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Confronting "confounding by health system use" in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment.

ABSTRACT: Under Medicare Part D, patient characteristics influence plan choice, which in turn influences Part D coverage gap entry. We compared predefined propensity score (PS) and high-dimensional propensity score (hdPS) approaches to address such "confounding by health system use" in assessing whether coverage gap entry is associated with cardiovascular events or death.We followed 243,079 Medicare patients aged 65+ years with linked prescription, medical, and plan-specific data in 2005-2007. Patients reached the coverage gap and were followed until an event or year's end. Exposed patients were responsible for drug costs in the gap; unexposed patients (patients with non-Part D drug insurance and Part D patients receiving a low-income subsidy) received financial assistance. Exposed patients were 1:1 PS-matched or hdPS-matched to unexposed patients. The PS model included 52 predefined covariates; the hdPS model added 400 empirically identified covariates. Hazard ratios for death and any of five cardiovascular outcomes were compared. In sensitivity analyses, we explored residual confounding using only low-income subsidy patients in the unexposed group.In unadjusted analyses, exposed patients had no greater hazard of death (HR?=?1.00; 95%CI, 0.84-1.20) or other outcomes. PS-matched (HR?=?1.29; 0.99-1.66) and hdPS-matched (HR?=?1.11; 0.86-1.42) analyses showed elevated but non-significant hazards of death. In sensitivity analyses, the PS analysis showed a protective effect (HR?=?0.78; 0.61-0.98), whereas the hdPS analysis (HR?=?1.06; 0.82-1.37) confirmed the main hdPS findings.Although the PS-matched analysis suggested elevated but non-significant hazards of death among patients with no financial assistance during the gap, the hdPS analysis produced lower estimates that were stable across sensitivity analyses.

SUBMITTER: Polinski JM

PROVIDER: S-EPMC3367305 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Confronting "confounding by health system use" in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment.

Polinski Jennifer M JM Schneeweiss Sebastian S Glynn Robert J RJ Lii Joyce J Rassen Jeremy A JA

Pharmacoepidemiology and drug safety 20120501

<h4>Purpose</h4>Under Medicare Part D, patient characteristics influence plan choice, which in turn influences Part D coverage gap entry. We compared predefined propensity score (PS) and high-dimensional propensity score (hdPS) approaches to address such "confounding by health system use" in assessing whether coverage gap entry is associated with cardiovascular events or death.<h4>Methods</h4>We followed 243,079 Medicare patients aged 65+ years with linked prescription, medical, and plan-specifi ...[more]

PMID: 22552984

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Project description:ImportanceRisk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement.ObjectiveTo examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures.Design, setting, and participantsThis comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018.Main outcomes and measuresThe main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes.ResultsThere were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers.Conclusions and relevanceIncorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Dataset Information

Confronting "confounding by health system use" in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment.

Publications

Confronting "confounding by health system use" in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment.

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